scRNA-seq Analysis Workflow
A step-by-step tutorial using Seurat, Harmony, and SingleR
Sai Harshitha Muddamsetty
2025-05-27
Setup
Install Required Packages
If you don’t have the required packages installed, you can run the following code chunks to install them.
Install CRAN packages
install.packages(c("dplyr", "Seurat", "patchwork", "harmony", "tidyverse", "ggplot2"))###Install Bioconductor packages
BiocManager::install(c("celldex", "SingleR", "scran"))Load Required Packages
#Load necessary libraries for scRNA-seq analysis
library(dplyr)
library(Seurat)
library(patchwork)
library(harmony)
library(tidyverse)
library(ggplot2)
library(celldex)
library(SingleR)
library(scran)Load Sample Data
Make sure your data folders contain the files named exactly as barcodes.tsv, features.tsv, and matrix.mtx because Read10X() expects these filenames.
# Load sample data using Read10X; adjust paths to where your files are stored
sam1 <- Read10X("~/Documents/Drive G/Personal Project/sc_RNAseq tutorial/sample/sample_1")
sam2 <- Read10X("~/Documents/Drive G/Personal Project/sc_RNAseq tutorial/sample/sample_2")Convert the Files into Seurat Objects
Convert the raw data matrices into Seurat objects with minimum filtering criteria.
sam1 <- CreateSeuratObject(counts = sam1, min.cells = 3, min.features = 200, project = "sam1")
sam2 <- CreateSeuratObject(counts = sam2, min.cells= 3, min.features = 200, project ="sam2")Calculate Mitochondrial Gene Percentage
sam1[["percent.mt"]] <- PercentageFeatureSet(sam1, pattern = "^MT-")
sam2[["percent.mt"]] <- PercentageFeatureSet(sam2, pattern = "^MT-")
# Uncomment to view metadata in RStudio's viewer
# View(sam1@meta.data)
# View(sam2@meta.data)Visualize QC Metrics Before Filtering
#Violin Plots of QC Metrics
VlnPlot(sam1, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) 
VlnPlot(sam2, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) 
Note: These violin plots help assess data quality and guide you in
determining appropriate parameters for quality control (QC)
filtering.
Perform Quality Control (QC) Filtering
Here we subset the data to keep cells with:
- Number of detected features (genes) between 200 and 6500
- Mitochondrial gene percentage below 15%
sam1 <- subset(sam1, subset = nFeature_RNA > 200 & nFeature_RNA < 6500 & percent.mt < 15)
sam2 <- subset(sam2, subset = nFeature_RNA > 200 & nFeature_RNA < 6500 & percent.mt < 15)Visualize QC Metrics After Filtering
The violin plots below show the distribution of features, counts, and mitochondrial content after QC filtering.
# Violin plots after QC filtering
VlnPlot(sam1, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(sam2, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
## Merge Samples
Now that QC has been performed on each sample individually, we merge them into one Seurat object for downstream processing.
Merged_sam <- merge(sam1, sam2, add.cell.ids = c("sam1", "sam2"), project = "Merged_sam")##Pre-processing
# Normalize the merged data
Merged_sam <- NormalizeData(Merged_sam, verbose = FALSE)
# Identify 2000 highly variable features
Merged_sam <- FindVariableFeatures(Merged_sam, selection.method = "vst", nfeatures = 2000, verbose = FALSE)
# Scale the data
Merged_sam <- ScaleData(Merged_sam, verbose = FALSE)##Principal Component Analysis (PCA) We now perform PCA on the merged dataset to reduce dimensionality and visualize variance across principal components.
# Run PCA on the merged object
Merged_sam <- RunPCA(Merged_sam, verbose = FALSE)
# PCA plots
DimPlot(Merged_sam, reduction = "pca")
DimPlot(Merged_sam, reduction = "pca", dims = c(1, 5))
# Visualize PCA loadings
VizDimLoadings(Merged_sam, dims = 1:2, reduction = "pca")
## Run
UMAP Before Harmony Integration
Merged_sam <- RunUMAP(Merged_sam, reduction = "pca", dims = 1:30, verbose = FALSE)
plot1 <- DimPlot(Merged_sam, reduction = "umap", group.by = "orig.ident", pt.size = 0.5) +
ggtitle("UMAP Before Harmony Integration")Run Harmony Integration
options(repr.plot.height = 2.5, repr.plot.width = 6)
Merged_sam <- RunHarmony(Merged_sam, "orig.ident", plot_convergence = TRUE, verbose = FALSE)
Run UMAP After Harmony Integration
Merged_sam <- RunUMAP(Merged_sam, reduction = "harmony", dims = 1:30,
reduction.name = "umap.harmony", verbose = FALSE)
plot2 <- DimPlot(Merged_sam, reduction = "umap.harmony", group.by = "orig.ident", pt.size = 0.5) +
ggtitle("UMAP After Harmony Integration")
plot1 + plot2
### Run Clustering We use the Harmony reduction for neighbor detection
and clustering. The resolution parameter controls cluster
granularity:
- Higher resolution (e.g., 0.5) gives more clusters (finer
granularity)
- Lower resolution (e.g., 0.1) gives fewer clusters (coarser
granularity)
Merged_sam <- FindNeighbors(Merged_sam, reduction = "harmony", dims = 1:30, verbose = FALSE)
Merged_sam <- FindClusters(Merged_sam, resolution = 0.5, verbose = FALSE)
Merged_sam <- FindClusters(Merged_sam, resolution = 0.1, verbose = FALSE)
# Visualize clusters based on the lower-resolution clustering
DimPlot(Merged_sam, reduction = "umap.harmony",
group.by = "RNA_snn_res.0.1", label.size = 2)
Find Marker Genes
Join Layers
We use JoinLayers() to consolidate data layers within the Seurat object. This ensures all assay-related information is accessible for downstream functions such as FindAllMarkers() and differential expression analysis, especially if the object was previously split or had multiple layers (e.g., when processed with newer Seurat versions or spatial datasets).
Merged_sam <- JoinLayers(Merged_sam)Find Cluster-Specific Marker Genes
We identify marker genes that are upregulated in each cluster using FindAllMarkers().
Merged_sam.markers <- FindAllMarkers(Merged_sam, only.pos = TRUE, verbose = FALSE)Filter Upregulated Marker Genes (avg_log2FC > 1)
We retain genes with an average log2 fold change > 1 and extract the top 10 upregulated genes per cluster.
top_markers <- Merged_sam.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1)
write.csv(top_markers, file = "top_markers.csv", row.names = FALSE)
top10 <- Merged_sam.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1) %>%
slice_head(n = 10) %>%
ungroup()Visualize Marker Gene Expression
We visualize the expression of selected upregulated genes across clusters using FeaturePlot and summarize the expression of top 10 marker genes per cluster with a heatmap.
# Feature plots for selected upregulated genes
FeaturePlot(Merged_sam, features = c("S100P", "SLC11A1", "C1R", "KLRB1", "TRAC",
"NUSAP1", "HLA-DQB2", "TCL1A"))
# Heatmap of top 10 upregulated marker genes per cluster
DoHeatmap(Merged_sam, features = top10$gene) + NoLegend()
## Cell Type Annotation Using SingleR
Extract Normalized and Raw Counts
We extract the raw and normalized counts from the Seurat object for downstream SingleR analysis.
raw_counts <- LayerData(Merged_sam, assay = "RNA", layer = 'counts')
raw_counts[c("S100P", "SLC11A1", "C1R", "KLRB1", "TRAC"), 1:2]## 5 x 2 sparse Matrix of class "dgCMatrix"
## sam1_AAACCCAAGACGGTTG-1 sam1_AAACCCAAGGTCACAG-1
## S100P 4 .
## SLC11A1 . .
## C1R 34 .
## KLRB1 . .
## TRAC . .norm_counts <- LayerData(Merged_sam, assay = "RNA", layer = 'data')
norm_counts[c("S100P", "SLC11A1", "C1R", "KLRB1", "TRAC"), 1:2]## 5 x 2 sparse Matrix of class "dgCMatrix"
## sam1_AAACCCAAGACGGTTG-1 sam1_AAACCCAAGGTCACAG-1
## S100P 1.041993 .
## SLC11A1 . .
## C1R 2.809182 .
## KLRB1 . .
## TRAC . .Load Reference Dataset from celldex
We use the Human Primary Cell Atlas as the reference dataset for cell type annotation.
ref <- celldex::HumanPrimaryCellAtlasData()
unique(ref$label.main)## [1] "DC" "Smooth_muscle_cells" "Epithelial_cells"
## [4] "B_cell" "Neutrophils" "T_cells"
## [7] "Monocyte" "Erythroblast" "BM & Prog."
## [10] "Endothelial_cells" "Gametocytes" "Neurons"
## [13] "Keratinocytes" "HSC_-G-CSF" "Macrophage"
## [16] "NK_cell" "Embryonic_stem_cells" "Tissue_stem_cells"
## [19] "Chondrocytes" "Osteoblasts" "BM"
## [22] "Platelets" "Fibroblasts" "iPS_cells"
## [25] "Hepatocytes" "MSC" "Neuroepithelial_cell"
## [28] "Astrocyte" "HSC_CD34+" "CMP"
## [31] "GMP" "MEP" "Myelocyte"
## [34] "Pre-B_cell_CD34-" "Pro-B_cell_CD34+" "Pro-Myelocyte"unique(ref$label.fine)## [1] "DC:monocyte-derived:immature"
## [2] "DC:monocyte-derived:Galectin-1"
## [3] "DC:monocyte-derived:LPS"
## [4] "DC:monocyte-derived"
## [5] "Smooth_muscle_cells:bronchial:vit_D"
## [6] "Smooth_muscle_cells:bronchial"
## [7] "Epithelial_cells:bronchial"
## [8] "B_cell"
## [9] "Neutrophil"
## [10] "T_cell:CD8+_Central_memory"
## [11] "T_cell:CD8+"
## [12] "T_cell:CD4+"
## [13] "T_cell:CD8+_effector_memory_RA"
## [14] "T_cell:CD8+_effector_memory"
## [15] "T_cell:CD8+_naive"
## [16] "Monocyte"
## [17] "Erythroblast"
## [18] "BM"
## [19] "DC:monocyte-derived:rosiglitazone"
## [20] "DC:monocyte-derived:AM580"
## [21] "DC:monocyte-derived:rosiglitazone/AGN193109"
## [22] "DC:monocyte-derived:anti-DC-SIGN_2h"
## [23] "Endothelial_cells:HUVEC"
## [24] "Endothelial_cells:HUVEC:Borrelia_burgdorferi"
## [25] "Endothelial_cells:HUVEC:IFNg"
## [26] "Endothelial_cells:lymphatic"
## [27] "Endothelial_cells:HUVEC:Serum_Amyloid_A"
## [28] "Endothelial_cells:lymphatic:TNFa_48h"
## [29] "T_cell:effector"
## [30] "T_cell:CCR10+CLA+1,25(OH)2_vit_D3/IL-12"
## [31] "T_cell:CCR10-CLA+1,25(OH)2_vit_D3/IL-12"
## [32] "Gametocytes:spermatocyte"
## [33] "DC:monocyte-derived:A._fumigatus_germ_tubes_6h"
## [34] "Neurons:ES_cell-derived_neural_precursor"
## [35] "Keratinocytes"
## [36] "Keratinocytes:IL19"
## [37] "Keratinocytes:IL20"
## [38] "Keratinocytes:IL22"
## [39] "Keratinocytes:IL24"
## [40] "Keratinocytes:IL26"
## [41] "Keratinocytes:KGF"
## [42] "Keratinocytes:IFNg"
## [43] "Keratinocytes:IL1b"
## [44] "HSC_-G-CSF"
## [45] "DC:monocyte-derived:mature"
## [46] "Monocyte:anti-FcgRIIB"
## [47] "Macrophage:monocyte-derived:IL-4/cntrl"
## [48] "Macrophage:monocyte-derived:IL-4/Dex/cntrl"
## [49] "Macrophage:monocyte-derived:IL-4/Dex/TGFb"
## [50] "Macrophage:monocyte-derived:IL-4/TGFb"
## [51] "Monocyte:leukotriene_D4"
## [52] "NK_cell"
## [53] "NK_cell:IL2"
## [54] "Embryonic_stem_cells"
## [55] "Tissue_stem_cells:iliac_MSC"
## [56] "Chondrocytes:MSC-derived"
## [57] "Osteoblasts"
## [58] "Tissue_stem_cells:BM_MSC"
## [59] "Osteoblasts:BMP2"
## [60] "Tissue_stem_cells:BM_MSC:BMP2"
## [61] "Tissue_stem_cells:BM_MSC:TGFb3"
## [62] "DC:monocyte-derived:Poly(IC)"
## [63] "DC:monocyte-derived:CD40L"
## [64] "DC:monocyte-derived:Schuler_treatment"
## [65] "DC:monocyte-derived:antiCD40/VAF347"
## [66] "Tissue_stem_cells:dental_pulp"
## [67] "T_cell:CD4+_central_memory"
## [68] "T_cell:CD4+_effector_memory"
## [69] "T_cell:CD4+_Naive"
## [70] "Smooth_muscle_cells:vascular"
## [71] "Smooth_muscle_cells:vascular:IL-17"
## [72] "Platelets"
## [73] "Epithelial_cells:bladder"
## [74] "Macrophage:monocyte-derived"
## [75] "Macrophage:monocyte-derived:M-CSF"
## [76] "Macrophage:monocyte-derived:M-CSF/IFNg"
## [77] "Macrophage:monocyte-derived:M-CSF/Pam3Cys"
## [78] "Macrophage:monocyte-derived:M-CSF/IFNg/Pam3Cys"
## [79] "Macrophage:monocyte-derived:IFNa"
## [80] "Gametocytes:oocyte"
## [81] "Monocyte:F._tularensis_novicida"
## [82] "Endothelial_cells:HUVEC:B._anthracis_LT"
## [83] "B_cell:Germinal_center"
## [84] "B_cell:Plasma_cell"
## [85] "B_cell:Naive"
## [86] "B_cell:Memory"
## [87] "DC:monocyte-derived:AEC-conditioned"
## [88] "Tissue_stem_cells:lipoma-derived_MSC"
## [89] "Tissue_stem_cells:adipose-derived_MSC_AM3"
## [90] "Endothelial_cells:HUVEC:FPV-infected"
## [91] "Endothelial_cells:HUVEC:PR8-infected"
## [92] "Endothelial_cells:HUVEC:H5N1-infected"
## [93] "Macrophage:monocyte-derived:S._aureus"
## [94] "Fibroblasts:foreskin"
## [95] "iPS_cells:skin_fibroblast-derived"
## [96] "iPS_cells:skin_fibroblast"
## [97] "T_cell:gamma-delta"
## [98] "Monocyte:CD14+"
## [99] "Macrophage:Alveolar"
## [100] "Macrophage:Alveolar:B._anthacis_spores"
## [101] "Neutrophil:inflam"
## [102] "iPS_cells:PDB_fibroblasts"
## [103] "iPS_cells:PDB_1lox-17Puro-5"
## [104] "iPS_cells:PDB_1lox-17Puro-10"
## [105] "iPS_cells:PDB_1lox-21Puro-20"
## [106] "iPS_cells:PDB_1lox-21Puro-26"
## [107] "iPS_cells:PDB_2lox-5"
## [108] "iPS_cells:PDB_2lox-22"
## [109] "iPS_cells:PDB_2lox-21"
## [110] "iPS_cells:PDB_2lox-17"
## [111] "iPS_cells:CRL2097_foreskin"
## [112] "iPS_cells:CRL2097_foreskin-derived:d20_hepatic_diff"
## [113] "iPS_cells:CRL2097_foreskin-derived:undiff."
## [114] "B_cell:CXCR4+_centroblast"
## [115] "B_cell:CXCR4-_centrocyte"
## [116] "Endothelial_cells:HUVEC:VEGF"
## [117] "iPS_cells:fibroblasts"
## [118] "iPS_cells:fibroblast-derived:Direct_del._reprog"
## [119] "iPS_cells:fibroblast-derived:Retroviral_transf"
## [120] "Endothelial_cells:lymphatic:KSHV"
## [121] "Endothelial_cells:blood_vessel"
## [122] "Monocyte:CD16-"
## [123] "Monocyte:CD16+"
## [124] "Tissue_stem_cells:BM_MSC:osteogenic"
## [125] "Hepatocytes"
## [126] "Neutrophil:uropathogenic_E._coli_UTI89"
## [127] "Neutrophil:commensal_E._coli_MG1655"
## [128] "MSC"
## [129] "Neuroepithelial_cell:ESC-derived"
## [130] "Astrocyte:Embryonic_stem_cell-derived"
## [131] "Endothelial_cells:HUVEC:IL-1b"
## [132] "HSC_CD34+"
## [133] "CMP"
## [134] "GMP"
## [135] "B_cell:immature"
## [136] "MEP"
## [137] "Myelocyte"
## [138] "Pre-B_cell_CD34-"
## [139] "Pro-B_cell_CD34+"
## [140] "Pro-Myelocyte"
## [141] "Smooth_muscle_cells:umbilical_vein"
## [142] "iPS_cells:foreskin_fibrobasts"
## [143] "iPS_cells:iPS:minicircle-derived"
## [144] "iPS_cells:adipose_stem_cells"
## [145] "iPS_cells:adipose_stem_cell-derived:lentiviral"
## [146] "iPS_cells:adipose_stem_cell-derived:minicircle-derived"
## [147] "Fibroblasts:breast"
## [148] "Monocyte:MCSF"
## [149] "Monocyte:CXCL4"
## [150] "Neurons:adrenal_medulla_cell_line"
## [151] "Tissue_stem_cells:CD326-CD56+"
## [152] "NK_cell:CD56hiCD62L+"
## [153] "T_cell:Treg:Naive"
## [154] "Neutrophil:LPS"
## [155] "Neutrophil:GM-CSF_IFNg"
## [156] "Monocyte:S._typhimurium_flagellin"
## [157] "Neurons:Schwann_cell"Run SingleR for Cell Type Annotation
We run SingleR using normalized counts and the reference dataset to predict cell types.
ct_ann <- SingleR(test = norm_counts, # alternatively use raw_counts or SingleCellExperiment
ref = ref,
labels = ref$label.main,
de.method = 'wilcox')Explore Annotation Results
head(ct_ann)## DataFrame with 6 rows and 4 columns
## scores labels
## <matrix> <character>
## sam1_AAACCCAAGACGGTTG-1 0.403145:0.345071:0.349268:... Chondrocytes
## sam1_AAACCCAAGGTCACAG-1 0.166320:0.188311:0.200635:... Macrophage
## sam1_AAACCCAAGTGCGTCC-1 0.173666:0.196706:0.213181:... Pre-B_cell_CD34-
## sam1_AAACCCACACGCTATA-1 0.127044:0.168634:0.181017:... DC
## sam1_AAACCCAGTTTCCCAC-1 0.161358:0.172904:0.179275:... Macrophage
## sam1_AAACCCATCTTGATTC-1 0.201712:0.266665:0.320877:... Macrophage
## delta.next pruned.labels
## <numeric> <character>
## sam1_AAACCCAAGACGGTTG-1 0.053144843 Chondrocytes
## sam1_AAACCCAAGGTCACAG-1 0.004849993 Macrophage
## sam1_AAACCCAAGTGCGTCC-1 0.000941892 Pre-B_cell_CD34-
## sam1_AAACCCACACGCTATA-1 0.002488869 DC
## sam1_AAACCCAGTTTCCCAC-1 0.001034198 Macrophage
## sam1_AAACCCATCTTGATTC-1 0.204236805 Macrophageunique(ct_ann$pruned.labels)## [1] "Chondrocytes" "Macrophage" "Pre-B_cell_CD34-"
## [4] "DC" "Fibroblasts" "Pro-B_cell_CD34+"
## [7] "Epithelial_cells" "GMP" "MSC"
## [10] "T_cells" "B_cell" "NK_cell"
## [13] "Hepatocytes" "HSC_-G-CSF" "Monocyte"
## [16] "Smooth_muscle_cells" "HSC_CD34+" "Endothelial_cells"
## [19] NA "Pro-Myelocyte" "Neutrophils"
## [22] "Platelets" "Osteoblasts" "Keratinocytes"
## [25] "Tissue_stem_cells" "Embryonic_stem_cells" "Neurons"
## [28] "CMP" "Myelocyte" "iPS_cells"
## [31] "Neuroepithelial_cell" "BM" "Astrocyte"
## [34] "BM & Prog." "Erythroblast" "MEP"
## [37] "Gametocytes"table(ct_ann$pruned.labels)##
## Astrocyte B_cell BM
## 5 70 148
## BM & Prog. Chondrocytes CMP
## 13 129 152
## DC Embryonic_stem_cells Endothelial_cells
## 441 21 78
## Epithelial_cells Erythroblast Fibroblasts
## 1648 4 700
## Gametocytes GMP Hepatocytes
## 1 182 135
## HSC_-G-CSF HSC_CD34+ iPS_cells
## 182 53 9
## Keratinocytes Macrophage MEP
## 56 2964 8
## Monocyte MSC Myelocyte
## 312 219 26
## Neuroepithelial_cell Neurons Neutrophils
## 7 33 16
## NK_cell Osteoblasts Platelets
## 1367 15 6
## Pre-B_cell_CD34- Pro-B_cell_CD34+ Pro-Myelocyte
## 515 97 108
## Smooth_muscle_cells T_cells Tissue_stem_cells
## 935 1436 33print("All labels:")## [1] "All labels:"table(ct_ann$labels)##
## Astrocyte B_cell BM
## 6 70 148
## BM & Prog. Chondrocytes CMP
## 13 139 152
## DC Embryonic_stem_cells Endothelial_cells
## 441 21 79
## Epithelial_cells Erythroblast Fibroblasts
## 1648 4 758
## Gametocytes GMP Hepatocytes
## 1 182 135
## HSC_-G-CSF HSC_CD34+ iPS_cells
## 182 53 9
## Keratinocytes Macrophage MEP
## 56 3004 9
## Monocyte MSC Myelocyte
## 312 226 26
## Neuroepithelial_cell Neurons Neutrophils
## 7 33 16
## NK_cell Osteoblasts Platelets
## 1367 19 7
## Pre-B_cell_CD34- Pro-B_cell_CD34+ Pro-Myelocyte
## 515 97 110
## Smooth_muscle_cells T_cells Tissue_stem_cells
## 998 1439 33summary(is.na(ct_ann$pruned.labels))## Mode FALSE TRUE
## logical 12124 191Visualization of SingleR Scores
plotScoreHeatmap(ct_ann)
plotDeltaDistribution(ct_ann, ncol = 4, dots.on.top = FALSE)
Add SingleR Labels to Seurat Object Metadata
We add the pruned SingleR labels to the Seurat object metadata for visualization.
rownames(ct_ann)[1:2] # verify cell IDs## [1] "sam1_AAACCCAAGACGGTTG-1" "sam1_AAACCCAAGGTCACAG-1"Merged_sam <- AddMetaData(Merged_sam, ct_ann$pruned.labels, col.name = 'SingleR_HCA')Visualize Cell Type Annotations on UMAP
Merged_sam <- SetIdent(Merged_sam, value = "SingleR_HCA")
DimPlot(Merged_sam, label = TRUE, repel = TRUE, label.size = 3) + NoLegend()
Session Info
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.0 (2024-04-24)
## os macOS 15.5
## system x86_64, darwin20
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2025-05-29
## pandoc 3.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/x86_64/ (via rmarkdown)
## quarto 1.5.57 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/quarto
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-8 2024-09-12 [1] CRAN (R 4.4.1)
## alabaster.base 1.6.1 2024-11-10 [1] Bioconductor 3.20 (R 4.4.1)
## alabaster.matrix 1.6.1 2024-11-20 [1] Bioconductor 3.20 (R 4.4.2)
## alabaster.ranges 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## alabaster.schemas 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## alabaster.se 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## AnnotationDbi 1.68.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## AnnotationHub 3.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## beachmat 2.22.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## Biobase * 2.66.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## BiocFileCache 2.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## BiocGenerics * 0.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## BiocManager 1.30.25 2024-08-28 [1] CRAN (R 4.4.1)
## BiocNeighbors 2.0.1 2024-11-28 [1] Bioconductor 3.20 (R 4.4.2)
## BiocParallel 1.40.2 2025-04-08 [1] Bioconductor
## BiocSingular 1.22.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## BiocVersion 3.20.0 2024-10-21 [1] Bioconductor 3.20 (R 4.4.1)
## Biostrings 2.74.1 2024-12-16 [1] Bioconductor 3.20 (R 4.4.2)
## bit 4.6.0 2025-03-06 [1] CRAN (R 4.4.1)
## bit64 4.6.0-1 2025-01-16 [1] CRAN (R 4.4.1)
## blob 1.2.4 2023-03-17 [1] CRAN (R 4.4.0)
## bluster 1.16.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## bslib 0.9.0 2025-01-30 [1] CRAN (R 4.4.1)
## cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.0)
## celldex * 1.16.0 2024-10-31 [1] Bioconductor 3.20 (R 4.4.0)
## cli 3.6.5 2025-04-23 [1] CRAN (R 4.4.1)
## cluster 2.1.8.1 2025-03-12 [1] CRAN (R 4.4.1)
## codetools 0.2-20 2024-03-31 [1] CRAN (R 4.4.0)
## colorspace 2.1-1 2024-07-26 [1] CRAN (R 4.4.0)
## cowplot 1.1.3 2024-01-22 [1] CRAN (R 4.4.0)
## crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.0)
## curl 6.2.2 2025-03-24 [1] CRAN (R 4.4.1)
## data.table 1.17.0 2025-02-22 [1] CRAN (R 4.4.1)
## DBI 1.2.3 2024-06-02 [1] CRAN (R 4.4.0)
## dbplyr 2.5.0 2024-03-19 [1] CRAN (R 4.4.0)
## DelayedArray 0.32.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## DelayedMatrixStats 1.28.1 2025-01-09 [1] Bioconductor 3.20 (R 4.4.2)
## deldir 2.0-4 2024-02-28 [1] CRAN (R 4.4.0)
## digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1)
## dotCall64 1.2 2024-10-04 [1] CRAN (R 4.4.1)
## dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.4.0)
## dqrng 0.4.1 2024-05-28 [1] CRAN (R 4.4.0)
## edgeR 4.4.2 2025-01-27 [1] Bioconductor 3.20 (R 4.4.2)
## evaluate 1.0.3 2025-01-10 [1] CRAN (R 4.4.1)
## ExperimentHub 2.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## farver 2.1.2 2024-05-13 [1] CRAN (R 4.4.0)
## fastDummies 1.7.5 2025-01-20 [1] CRAN (R 4.4.1)
## fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
## filelock 1.0.3 2023-12-11 [1] CRAN (R 4.4.0)
## fitdistrplus 1.2-2 2025-01-07 [1] CRAN (R 4.4.1)
## forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.4.0)
## future * 1.40.0 2025-04-10 [1] CRAN (R 4.4.1)
## future.apply 1.11.3 2024-10-27 [1] CRAN (R 4.4.1)
## generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.0)
## GenomeInfoDb * 1.42.3 2025-01-27 [1] Bioconductor 3.20 (R 4.4.2)
## GenomeInfoDbData 1.2.13 2025-04-08 [1] Bioconductor
## GenomicRanges * 1.58.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## ggplot2 * 3.5.2 2025-04-09 [1] CRAN (R 4.4.1)
## ggrepel 0.9.6 2024-09-07 [1] CRAN (R 4.4.1)
## ggridges 0.5.6 2024-01-23 [1] CRAN (R 4.4.0)
## globals 0.17.0 2025-04-16 [1] CRAN (R 4.4.1)
## glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1)
## goftest 1.2-3 2021-10-07 [1] CRAN (R 4.4.0)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.4.0)
## gtable 0.3.6 2024-10-25 [1] CRAN (R 4.4.1)
## gypsum 1.2.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## harmony * 1.2.3 2024-11-27 [1] CRAN (R 4.4.1)
## HDF5Array 1.34.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.0)
## htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
## htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.0)
## httpuv 1.6.16 2025-04-16 [1] CRAN (R 4.4.1)
## httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.0)
## httr2 1.1.2 2025-03-26 [1] CRAN (R 4.4.1)
## ica 1.0-3 2022-07-08 [1] CRAN (R 4.4.0)
## igraph 2.1.4 2025-01-23 [1] CRAN (R 4.4.1)
## IRanges * 2.40.1 2024-12-05 [1] Bioconductor 3.20 (R 4.4.2)
## irlba 2.3.5.1 2022-10-03 [1] CRAN (R 4.4.0)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.4.0)
## jsonlite 2.0.0 2025-03-27 [1] CRAN (R 4.4.1)
## KEGGREST 1.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## KernSmooth 2.23-26 2025-01-01 [1] CRAN (R 4.4.1)
## knitr 1.50 2025-03-16 [1] CRAN (R 4.4.1)
## labeling 0.4.3 2023-08-29 [1] CRAN (R 4.4.0)
## later 1.4.2 2025-04-08 [1] CRAN (R 4.4.1)
## lattice 0.22-7 2025-04-02 [1] CRAN (R 4.4.1)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.4.0)
## lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0)
## limma 3.62.2 2025-01-09 [1] Bioconductor 3.20 (R 4.4.2)
## listenv 0.9.1 2024-01-29 [1] CRAN (R 4.4.0)
## lmtest 0.9-40 2022-03-21 [1] CRAN (R 4.4.0)
## locfit 1.5-9.12 2025-03-05 [1] CRAN (R 4.4.1)
## lubridate * 1.9.4 2024-12-08 [1] CRAN (R 4.4.1)
## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0)
## MASS 7.3-65 2025-02-28 [1] CRAN (R 4.4.1)
## Matrix 1.7-3 2025-03-11 [1] CRAN (R 4.4.1)
## MatrixGenerics * 1.18.1 2025-01-09 [1] Bioconductor 3.20 (R 4.4.2)
## matrixStats * 1.5.0 2025-01-07 [1] CRAN (R 4.4.1)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.0)
## metapod 1.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## mime 0.13 2025-03-17 [1] CRAN (R 4.4.1)
## miniUI 0.1.2 2025-04-17 [1] CRAN (R 4.4.1)
## nlme 3.1-168 2025-03-31 [1] CRAN (R 4.4.1)
## parallelly 1.43.0 2025-03-24 [1] CRAN (R 4.4.1)
## patchwork * 1.3.0 2024-09-16 [1] CRAN (R 4.4.1)
## pbapply 1.7-2 2023-06-27 [1] CRAN (R 4.4.0)
## pheatmap 1.0.12 2019-01-04 [1] CRAN (R 4.4.0)
## pillar 1.10.2 2025-04-05 [1] CRAN (R 4.4.1)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0)
## plotly 4.10.4 2024-01-13 [1] CRAN (R 4.4.0)
## plyr 1.8.9 2023-10-02 [1] CRAN (R 4.4.0)
## png 0.1-8 2022-11-29 [1] CRAN (R 4.4.0)
## polyclip 1.10-7 2024-07-23 [1] CRAN (R 4.4.0)
## presto 1.0.0 2025-01-08 [1] Github (immunogenomics/presto@7636b3d)
## progressr 0.15.1 2024-11-22 [1] CRAN (R 4.4.1)
## promises 1.3.2 2024-11-28 [1] CRAN (R 4.4.1)
## purrr * 1.0.4 2025-02-05 [1] CRAN (R 4.4.1)
## R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.4.0)
## R.oo 1.27.1 2025-05-02 [1] CRAN (R 4.4.1)
## R.utils 2.13.0 2025-02-24 [1] CRAN (R 4.4.1)
## R6 2.6.1 2025-02-15 [1] CRAN (R 4.4.1)
## RANN 2.6.2 2024-08-25 [1] CRAN (R 4.4.1)
## rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.4.0)
## RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.4.0)
## Rcpp * 1.0.14 2025-01-12 [1] CRAN (R 4.4.1)
## RcppAnnoy 0.0.22 2024-01-23 [1] CRAN (R 4.4.0)
## RcppHNSW 0.6.0 2024-02-04 [1] CRAN (R 4.4.0)
## readr * 2.1.5 2024-01-10 [1] CRAN (R 4.4.0)
## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.4.0)
## reticulate 1.42.0 2025-03-25 [1] CRAN (R 4.4.1)
## rhdf5 2.50.2 2025-01-09 [1] Bioconductor 3.20 (R 4.4.2)
## rhdf5filters 1.18.1 2025-03-06 [1] Bioconductor 3.20 (R 4.4.3)
## Rhdf5lib 1.28.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## RhpcBLASctl 0.23-42 2023-02-11 [1] CRAN (R 4.4.0)
## rlang 1.1.6 2025-04-11 [1] CRAN (R 4.4.1)
## rmarkdown 2.29 2024-11-04 [1] CRAN (R 4.4.1)
## ROCR 1.0-11 2020-05-02 [1] CRAN (R 4.4.0)
## RSpectra 0.16-2 2024-07-18 [1] CRAN (R 4.4.0)
## RSQLite 2.3.11 2025-05-04 [1] CRAN (R 4.4.1)
## rstudioapi 0.17.1 2024-10-22 [1] CRAN (R 4.4.1)
## rsvd 1.0.5 2021-04-16 [1] CRAN (R 4.4.0)
## Rtsne 0.17 2023-12-07 [1] CRAN (R 4.4.0)
## S4Arrays 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## S4Vectors * 0.44.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## sass 0.4.10 2025-04-11 [1] CRAN (R 4.4.1)
## ScaledMatrix 1.14.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## scales 1.4.0 2025-04-24 [1] CRAN (R 4.4.1)
## scattermore 1.2 2023-06-12 [1] CRAN (R 4.4.0)
## scran * 1.34.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## sctransform 0.4.2 2025-04-30 [1] CRAN (R 4.4.1)
## scuttle * 1.16.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## sessioninfo 1.2.3 2025-02-05 [1] CRAN (R 4.4.1)
## Seurat * 5.3.0 2025-04-23 [1] CRAN (R 4.4.1)
## SeuratObject * 5.1.0 2025-04-22 [1] CRAN (R 4.4.1)
## shiny 1.10.0 2024-12-14 [1] CRAN (R 4.4.1)
## SingleCellExperiment * 1.28.1 2024-11-10 [1] Bioconductor 3.20 (R 4.4.1)
## SingleR * 2.8.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## sp * 2.2-0 2025-02-01 [1] CRAN (R 4.4.1)
## spam 2.11-1 2025-01-20 [1] CRAN (R 4.4.1)
## SparseArray 1.6.2 2025-02-20 [1] Bioconductor 3.20 (R 4.4.2)
## sparseMatrixStats 1.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## spatstat.data 3.1-6 2025-03-17 [1] CRAN (R 4.4.1)
## spatstat.explore 3.4-2 2025-03-21 [1] CRAN (R 4.4.1)
## spatstat.geom 3.3-6 2025-03-18 [1] CRAN (R 4.4.1)
## spatstat.random 3.3-3 2025-03-19 [1] CRAN (R 4.4.1)
## spatstat.sparse 3.1-0 2024-06-21 [1] CRAN (R 4.4.0)
## spatstat.univar 3.1-2 2025-03-05 [1] CRAN (R 4.4.1)
## spatstat.utils 3.1-3 2025-03-15 [1] CRAN (R 4.4.1)
## statmod 1.5.0 2023-01-06 [1] CRAN (R 4.4.0)
## stringi 1.8.7 2025-03-27 [1] CRAN (R 4.4.1)
## stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.4.0)
## SummarizedExperiment * 1.36.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## survival 3.8-3 2024-12-17 [1] CRAN (R 4.4.1)
## tensor 1.5 2012-05-05 [1] CRAN (R 4.4.0)
## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.4.0)
## tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.4.0)
## tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0)
## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.4.0)
## timechange 0.3.0 2024-01-18 [1] CRAN (R 4.4.0)
## tzdb 0.5.0 2025-03-15 [1] CRAN (R 4.4.1)
## UCSC.utils 1.2.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## uwot 0.2.3 2025-02-24 [1] CRAN (R 4.4.1)
## vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
## viridis 0.6.5 2024-01-29 [1] CRAN (R 4.4.0)
## viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.4.0)
## withr 3.0.2 2024-10-28 [1] CRAN (R 4.4.1)
## xfun 0.52 2025-04-02 [1] CRAN (R 4.4.1)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.0)
## XVector 0.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.0)
## zlibbioc 1.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
## zoo 1.8-14 2025-04-10 [1] CRAN (R 4.4.1)
##
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## * ── Packages attached to the search path.
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